The present invention relates generally to manufacturing and, more particularly, to a method and apparatus for compensating metrology data for site bias prior to filtering.
Today's global market forces manufacturers of mass products to offer high quality products at a low price. It is thus important to improve yield and process efficiency to minimize production costs. This is especially true in the field of semiconductor fabrication where it is essential to combine cutting edge technology with mass production techniques. It is, therefore, the goal of semiconductor manufacturers to reduce the consumption of raw materials and consumables while at the same time improve product quality and process tool utilization. The latter aspect is especially important since the equipment used in modern semiconductor facilities is extremely costly and represents a dominant part of the total production costs. For example, in manufacturing modern integrated circuits, 500 or more individual processes may be necessary to complete an integrated circuit, wherein failure in a single process step may result in a loss of the complete integrated circuit. This problem is exacerbated when the size of substrates, on which a plurality of such integrated circuits are processed, steadily increases, so that failure in a single process step may entail the loss of a large number of products.
Therefore, the various manufacturing stages are thoroughly monitored to avoid undue waste of manpower, tool operation time, and raw materials. Ideally, the effect of each individual process step on each substrate would be detected by measurement and the substrate under consideration would be released for further processing only if the required specifications were met. However, such a process control strategy is not practical since measuring the effects of certain processes may require relatively long measurement times, frequently ex situ, or may even necessitate the destruction of the sample. Moreover, immense effort, in terms of time and equipment, would be required on the metrology side to provide the desired measurement results. Additionally, utilization of the process tool would be reduced since the tool would be released only after the provision of the measurement result and its assessment.
The introduction of statistical methods, also referred to as statistical process control (SPC), for adjusting process parameters significantly relaxes the above problem and allows a moderately high utilization of the process tools while attaining a relatively high product yield. Statistical process control is based on the monitoring of the process output to thereby identify an out-of-control situation, wherein a causal relationship is established to an external disturbance. After occurrence of an out-of-control situation, operator interaction is usually required to manipulate a process parameter so as to return to an in-control situation, wherein the causal relationship may be helpful in selecting an appropriate control action. Nevertheless, in total, a large number of dummy substrates or pilot substrates may be necessary to adjust process parameters of respective process tools, wherein parameter drifts during the process have to be taken into consideration when designing a process sequence, since such parameter drifts may remain undetected over a long time period or may not be efficiently compensated for by SPC techniques.
More recently, a process control strategy has been introduced, and is continuously improving, that allows a high degree of process control, desirably on a run-to-run basis, with a moderate amount of measurement data. In this control strategy, so-called advanced process control (APC), a model of a process or of a group of interrelated processes is established and implemented in an appropriately configured process controller. The process controller also receives information which may include pre-process measurement data and/or post-process measurement data, as well as information related, for instance, to the substrate history, such as type of process or processes, the product type, the process tool or process tools in which the products are to be processed or have been processed in previous steps, the process recipe to be used (i.e., a set of required steps for the process or processes under consideration, wherein possibly fixed process parameters and variable process parameters may be contained), and the like. From this information and the process model, the process controller determines a controller state or process state that describes the effect of the process or processes under consideration on the specific product, thereby permitting the establishment of an appropriate parameter setting of the variable parameters of the specified process recipe to be performed with the substrate under consideration, wherein tool-specific internal or “low-rank” control units (substantially) maintain the parameter values, such as flow rates, temperatures, exposure doses and the like, at the targets specified by the APC controller. Thus, the APC controller may have a predictive behavior, whose accuracy may depend on the amount of measurement data and its timeliness with respect to the current process run.
Generally, in process control it is desirable to make process adjustments based on known good data (i.e., data representative of the process). To that end, data is typically filtered prior to processing to identify and, subsequently, ignore outlier data. Various outlier filters, such as box filters, absolute bounds filters, etc., may be used to perform this outlier rejection. For example, a box filter typically rejects data that falls more than a predetermined distance from the mean, such as one standard deviation. An absolute bounds filter rejects data that falls outside an absolute range from a target value, such as +/−3 nm. The filtered metrology data is then supplied to the process controller for determining the process state and/or adjusting the process parameters.
The unfiltered measurement data, however, may stem from different process tools performing equivalent processes, and/or only dedicated wafers or wafer sites that are subjected to measurement. In some cases, a particular tool may process wafers slightly differently than other similar tools, such that the measurements performed on wafers processed with the tool include a consistently higher or lower value than those processed on other tools. This mismatch is commonly referred to as tool bias. Another type of bias, referred to as site bias, may also exist across different sites on a particular wafer. For example, a particular etch or polishing tool may process wafers such that sites near the periphery of a wafer have consistently higher values than those in the center (i.e., referred to as a bowl or dished profile) or consistently lower values than those in the center (i.e., referred to as a domed profile).
In some instances, the wafer or site bias is so significant that the measurement data is routinely rejected by the outlier filter. Although such biased data is outside the bounds of the outlier filter, it does not actually represent the type of outlier data the filter is attempting to reject (e.g., data associated tool or material faults). As the data reflects normal operation it would be useful for controlling the process. Errant outlier rejection reduces the total amount of useful metrology data that may be employed for process control, thereby reducing its efficacy.
This section of this document is intended to introduce various aspects of art that may be related to various aspects of the present invention described and/or claimed below. This section provides background information to facilitate a better understanding of the various aspects of the present invention. It should be understood that the statements in this section of this document are to be read in this light, and not as admissions of prior art. The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.